| --- |
| frameworks: |
| - pytorch |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - OneScience |
| - FPINNs |
| - fuzzy-physics-informed-neural-networks |
| - Allen-Cahn |
| - forward-problem |
| - inverse-problem |
| tasks: |
| - pde-solving |
| --- |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">FPINNs</span> |
| </strong> |
| </p> |
| |
| # Model Overview |
|
|
| In this model package, FPINNs refers to Fuzzy Physics-Informed Neural Networks, not fractional PINNs. The architecture augments a fully connected network with a Gaussian fuzzy-membership branch and fuses neural features with fuzzy-rule features to predict solutions to partial differential equations. |
|
|
| The current example solves the Allen–Cahn equation: |
|
|
| ```text |
| u_t - lambda_1 u_xx + lambda_2 (u^3 - u) = 0 |
| ``` |
|
|
| Paper: Deep fuzzy physics-informed neural networks for forward and inverse PDE problems |
| https://doi.org/10.1016/j.neunet.2024.106750 |
|
|
| # Model Description |
|
|
| FPINNs are trained jointly on data loss and PDE residuals and support both forward and inverse Allen–Cahn problems. The forward task predicts the spatiotemporal solution for known parameters `lambda_1=0.0001` and `lambda_2=5.0`; the inverse task jointly learns the equation solution and both parameters from observations. The model is trained with Adam by default, with optional L-BFGS refinement. |
|
|
| # Use Cases |
|
|
| | Use Case | Description | |
| | :---: | :--- | |
| | Forward Allen–Cahn problem | Predict the complete spatiotemporal solution using known diffusion and reaction parameters | |
| | Inverse Allen–Cahn problem | Identify diffusion and reaction parameters from solution observations | |
| | Fuzzy-feature research | Evaluate the fusion of neural features and Gaussian fuzzy-rule features | |
| | Pipeline validation | Validate training and inference using the bundled data and a small-scale configuration | |
|
|
| # Usage |
|
|
| ## 1. OneCode |
|
|
| Use the online OneCode environment for an intelligent, one-click AI for Science (AI4S) programming experience: |
|
|
| [Launch OneCode for one-click AI4S programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home) |
|
|
| ## 2. Manual Setup |
|
|
| **Hardware Requirements** |
|
|
| - A GPU or DCU is recommended for training and full-grid inference. |
| - A CPU can be used for small-scale pipeline validation. |
| - DCU users must install DTK and a PyTorch environment compatible with the target cluster. |
|
|
| ### Download the Model Package |
|
|
| ```bash |
| modelscope download --model OneScience/FPINNs --local_dir ./FPINNs |
| cd FPINNs |
| ``` |
|
|
| ### Set Up the Runtime Environment |
|
|
| **DCU Environment** |
|
|
| ```bash |
| # Activate DTK and Conda first |
| conda create -n onescience311 python=3.11 -y |
| conda activate onescience311 |
| pip install onescience[cfd-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| ``` |
|
|
| **GPU Environment** |
|
|
| ```bash |
| # Activate Conda first |
| conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12 |
| conda activate onescience311 |
| pip install onescience[cfd-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| ``` |
|
|
| ### Training Data |
|
|
| The model package includes the Allen–Cahn data file `data/AC.mat`, which contains spatial coordinates, temporal coordinates, and the corresponding equation solution. The number of training samples and evaluation batch size can be adjusted in `conf/config.yaml`. |
|
|
| ### Training |
|
|
| The training task is controlled by `common.task` in `conf/config.yaml`: `forward` selects the forward problem, while `inverse` selects the inverse problem. After configuring the task, run: |
|
|
| ```bash |
| python scripts/train.py |
| ``` |
|
|
| Training checkpoints and histories are saved to the `weight/` and `result/` directories by default. |
|
|
| ### Model Weights |
|
|
| This repository provides weights trained on the Allen–Cahn dataset in the `weight/` directory. |
|
|
| ### Inference, Evaluation, and Visualization |
|
|
| After training the selected task, run: |
|
|
| ```bash |
| python scripts/inference.py |
| ``` |
|
|
| The inference task is also controlled by `common.task`. Results are saved as `result/fpinn_forward.*` or `result/fpinn_inverse.*` by default. The inverse task additionally reports the recovered `lambda_1` and `lambda_2` values. Model, training, loss, and inference parameters can all be modified in `conf/config.yaml`. |
|
|
| # Official OneScience Resources |
|
|
| | Platform | OneScience Repository | Skills Repository | |
| | --- | --- | --- | |
| | Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills | |
| | GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills | |
|
|
| # Citations and License |
|
|
| - Wu, W., Duan, S., Sun, Y., Yu, Y., Liu, D., and Peng, D. Deep fuzzy physics-informed neural networks for forward and inverse PDE problems. Neural Networks, 181, 106750, 2025. |
| - This model package is released under the Apache-2.0 license and retains attribution to the original paper and data sources. |
|
|
